For insurance advisors, every customer interaction matters. Yet determining who to contact, when to engage, and which products to recommend often relies on individual judgment, creating inconsistent outcomes across teams and regions.

One of Canada’s largest insurance brokerages partnered with Lantern to create a data-driven recommendation engine that guides advisors toward the next best action for every customer interaction. The solution helps advisors prioritize the highest-value opportunities, balance workloads across branches, and drive stronger retention and revenue outcomes at scale.

The brokerage’s advisors manage customer relationships across a broad portfolio of insurance products. While the organization had experimented with scorecards and early modeling capabilities, there was no practical way to translate those insights into daily actions for advisors. As a result, advisors often relied on personal judgment to determine which customers to contact and what conversations to prioritize.

This approach created several challenges:

• Advisors lacked a consistent method for prioritizing customer outreach.

• Existing scorecards and models identified opportunities but did not provide a clear next action.

• Workloads varied significantly across branches, with some teams overwhelmed while others had available capacity.

• Revenue and retention opportunities could be missed because the right customer conversations were not happening at the right time.

The brokerage needed a scalable solution that could guide advisor activity across the organization, helping every advisor focus on the customers and opportunities most likely to create business value.

Lantern partnered with the brokerage to design and build a next-best-action recommendation engine that delivers daily guidance to advisors. The solution analyzes customer and business data to determine which customers should be contacted, when they should be contacted, which insurance advisor is best suited to contact the customer, and which products should be recommended.

The recommendation engine combines retention insights and revenue opportunity modeling to prioritize advisor actions and help teams focus on the highest-value customer interactions. It also distributes recommendations across advisors and branches, helping balance workloads nationwide rather than concentrating opportunities within a handful of locations.

To accelerate value, Lantern delivered an MVP in just four weeks that included:

• A retention model predicting the likelihood of policy renewal.

• A revenue model estimating premium opportunity.

• An optimization engine balancing retention and revenue objectives.

• Advisor-specific recommendations identifying who to contact and when.

• End-to-end workflows running from data inputs through to actionable recommendations.

As the solution continues to evolve, additional inputs such as renewal complexity, customer availability, and advisor availability further enhance recommendation quality and business impact.

Within four weeks, Lantern delivered a working end-to-end MVP into the brokerage’s test environment, demonstrating the viability of the recommendation engine and establishing a foundation for broader rollout across the organization.

The solution is designed to support more than 1,000 advisors across Canada, providing a consistent and scalable framework for prioritizing customer engagement opportunities. By combining retention insights, revenue opportunity, and operational considerations into a single recommendation engine, the brokerage can help advisors focus on the customers most likely to benefit from timely outreach and relevant coverage recommendations.

As additional business and operational inputs are incorporated into the model, the quality of recommendations continues to improve. The result is a more data-driven approach to customer retention, revenue growth, and workforce optimization across the brokerage’s nationwide advisor network.

Key outcomes included:

• Delivered a working MVP in four weeks.

• Built a next-best-action recommendation engine for insurance advisors.
• Designed to support more than 1,000 employees across Canada.
• Combined retention and revenue models to prioritize advisor actions.

• Reduced reliance on manual decision-making and advisor guesswork.

• Improved workload distribution across branches nationwide.

• Created a scalable foundation for future optimization and nationwide adoption.